A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices

Aamo Iorliam

doi:10.35377/saucis...1354791

EN

A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices

Abstract

The inter-class classification and source identification of IoT devices has been studied by several researchers recently due to the vast amount of available IoT devices and the huge amount of data these IoT devices generate almost every minute. As such there is every need to identify the source where the IoT data is generated and also separate an IoT device from the other using on the data they generate. This paper proposes a novel additive IoT features with the CNN system for the purpose of IoT source identification and classification. Experimental results shows that indeed the proposed method is very effective achieving an overall classification and source identification accuracy of 99.67 %. This result has a practical application to forensics purposes due to the fact that accurately identifying and classifying the source of an IoT device via the generated data can link organisations/persons to the activities they perform over the network. As such ensuring accountability and responsibility by IoT device users.

Keywords

References

[1] A. Iorliam, A.T.S. Ho, A. Waller, and X. Zhao. "Using benford’s law divergence and neural networks for classification and source identification of biometric images." In Digital Forensics and Watermarking: 15th International Workshop, IWDW 2016, Beijing, China, September 17-19, 2016, Revised Selected Papers 15, pp. 88105. Springer International Publishing, 2017.
[2] J. Kotak, and E. Yuval. "IoT device identification using deep learning." 13th International Conference on Computational Intelligence in Security for Information Systems (CISIS 2020) 12. Springer International Publishing, 2021.
[3] C. Koball, P.R. Bhaskar, W. Yong, S. Tyler, and F. Connor "IoT Device Identification Using Unsupervised Machine Learning." Information 14.6, 2023
[4] A. Iorliam, A. Application of power laws to biometrics, forensics, and network traffic analysis. University of Surrey (United Kingdom), 2016.
[5] L. Bai, L. Yao, S. S. Kanhere, X. Wang, and Z. Yang. "Automatic device classification from network traffic streams of internet of things." 2018 IEEE 43rd conference on local computer networks (LCN). IEEE, 2018.
[6] I. Cvitić, D. Peraković, M. Periša, and B. Gupta. "Ensemble machine learning approach for classification of IoT devices in smart home." International Journal of Machine Learning and Cybernetics 12.11 (2021): 3179-3202.
[7] H. M. Zahid, Y. Saleem, F. Hayat, F. Z. Khan, R. Alroobaea, F. Almansour, M. Ahmad, and I. Ali. "A framework for identification and classification of iot devices for security analysis in heterogeneous network." Wireless Communications and Mobile Computing 2022 (2022).
[8] A. R. Zarzoor, N.A.S. Al-Jamali, and I.R.K. Al-Saedi. "Traffic Classification of IoT Devices by Utilizing Spike Neural Network Learning Approach." Mathematical Modelling of Engineering Problems 10.2 (2023).

Details

Primary Language

English

Subjects

Computer Software

Journal Section

Research Article

Authors

Aamo Iorliam ^*
0000-0001-8238-9686
Nigeria

Early Pub Date

December 27, 2023

Publication Date

December 31, 2023

Submission Date

September 4, 2023

Acceptance Date

November 15, 2023

Published in Issue

Year 2023 Volume: 6 Number: 3

DOI

https://doi.org/10.35377/saucis...1354791

IZ

https://izlik.org/JA45JP75HC

Cite

RIS / Bibtex

APA

Iorliam, A. (2023). A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices. Sakarya University Journal of Computer and Information Sciences, 6(3), 218-225. https://doi.org/10.35377/saucis...1354791

AMA

1.Iorliam A. A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices. SAUCIS. 2023;6(3):218-225. doi:10.35377/saucis.1354791

Chicago

Iorliam, Aamo. 2023. “A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices”. Sakarya University Journal of Computer and Information Sciences 6 (3): 218-25. https://doi.org/10.35377/saucis. 1354791.

EndNote

Iorliam A (December 1, 2023) A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices. Sakarya University Journal of Computer and Information Sciences 6 3 218–225.

IEEE

[1]A. Iorliam, “A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices”, SAUCIS, vol. 6, no. 3, pp. 218–225, Dec. 2023, doi: 10.35377/saucis...1354791.

ISNAD

Iorliam, Aamo. “A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices”. Sakarya University Journal of Computer and Information Sciences 6/3 (December 1, 2023): 218-225. https://doi.org/10.35377/saucis. 1354791.

JAMA

1.Iorliam A. A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices. SAUCIS. 2023;6:218–225.

MLA

Iorliam, Aamo. “A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices”. Sakarya University Journal of Computer and Information Sciences, vol. 6, no. 3, Dec. 2023, pp. 218-25, doi:10.35377/saucis. 1354791.

Vancouver

1.Aamo Iorliam. A Novel Additive Internet of Things (IoT) Features and Convolutional Neural Network for Classification and Source Identification of IoT Devices. SAUCIS. 2023 Dec. 1;6(3):218-25. doi:10.35377/saucis. 1354791